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      Quantification of biological aging in young adults.

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          Abstract

          Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.

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          Most cited references 41

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          Interventions to Slow Aging in Humans: Are We Ready?

          The workshop entitled ‘Interventions to Slow Aging in Humans: Are We Ready?’ was held in Erice, Italy, on October 8–13, 2013, to bring together leading experts in the biology and genetics of aging and obtain a consensus related to the discovery and development of safe interventions to slow aging and increase healthy lifespan in humans. There was consensus that there is sufficient evidence that aging interventions will delay and prevent disease onset for many chronic conditions of adult and old age. Essential pathways have been identified, and behavioral, dietary, and pharmacologic approaches have emerged. Although many gene targets and drugs were discussed and there was not complete consensus about all interventions, the participants selected a subset of the most promising strategies that could be tested in humans for their effects on healthspan. These were: (i) dietary interventions mimicking chronic dietary restriction (periodic fasting mimicking diets, protein restriction, etc.); (ii) drugs that inhibit the growth hormone/IGF-I axis; (iii) drugs that inhibit the mTOR–S6K pathway; or (iv) drugs that activate AMPK or specific sirtuins. These choices were based in part on consistent evidence for the pro-longevity effects and ability of these interventions to prevent or delay multiple age-related diseases and improve healthspan in simple model organisms and rodents and their potential to be safe and effective in extending human healthspan. The authors of this manuscript were speakers and discussants invited to the workshop. The following summary highlights the major points addressed and the conclusions of the meeting.
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            Revised formulas for summarizing retinal vessel diameters.

            Background/Purpose. Recent findings suggest that an objective assessment of retinal vessel caliber from fundus photographs provide information about the association of microvascular characteristics with macrovascular disease. Current methods used to quantify retinal vessel caliber, introduced by Parr(1,2) and Hubbard,(3) are not independent of scale and are affected by the number of vessels. To improve upon these methods we introduce revised formulas for quantifying vessel caliber. Methods. Revised formulas were estimated using retinal vessel measurements from 44 young adults free of hypertension and diabetes. Comparisons between the two methods were done using digitized photographs from 4926 participants at the baseline examination of the Beaver Dam Eye Study (BDES), an ongoing population-based cohort study initiated in 1987. Individual arterioles and venules were measured using semi-automated computer software from which summary measures were calculated. Results. Correlation coefficients between the Parr-Hubbard and revised formulas were high (Pearson correlation coefficients ranging from 0.94 to 0.98). Both arteriolar and venular caliber significantly increased with an increasing number of vessels measured using the Parr-Hubbard formulas (p 0.50). Conclusions. We describe revised formulas for summarizing retinal vessel diameters measured from fundus photographs to be used in future studies and analyses. The revised formulas correlate highly with the previously used Parr-Hubbard formulas, but offer the advantages of being more robust against variability in the number of vessels observed, being independent of image scale, and being easier to implement.
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              Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?

              Biological age (BA) is useful for examining differences in aging rates. Nevertheless, little consensus exists regarding optimal methods for calculating BA. The aim of this study is to compare the predictive ability of five BA algorithms. The sample included 9,389 persons, aged 30-75 years, from National Health and Nutrition Examination Survey III. During the 18-year follow-up, 1,843 deaths were counted. Each BA algorithm was compared with chronological age on the basis of predictive sensitivity and strength of association with mortality. Results found that the Klemera and Doubal method was the most reliable predictor of mortality and performed significantly better than chronological age. Furthermore, when included with chronological age in a model, Klemera and Doubal method had more robust predictive ability and caused chronological age to no longer be significantly associated with mortality. Given the potential of BA to highlight heterogeneity, the Klemera and Doubal method algorithm may be useful for studying a number of questions regarding the biology of aging.
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                Author and article information

                Journal
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences of the United States of America
                1091-6490
                0027-8424
                Jul 28 2015
                : 112
                : 30
                Affiliations
                [1 ] Department of Medicine, Duke University School of Medicine, Durham, NC 27710; Social Science Research Institute, Duke University, Durham, NC 27708; dbelsky@duke.edu.
                [2 ] Department of Psychology & Neuroscience, Duke University, Durham, NC 27708; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708; Center for Genomic and Computational Biology, Duke University, Durham, NC 27708; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, London SE5 8AF, United Kingdom;
                [3 ] Department of Psychology & Neuroscience, Duke University, Durham, NC 27708;
                [4 ] Department of Medicine, Duke University School of Medicine, Durham, NC 27710;
                [5 ] Center for Genomic and Computational Biology, Duke University, Durham, NC 27708;
                [6 ] Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, London SE5 8AF, United Kingdom; Department of Child & Adolescent Psychiatry, Institute of Psychiatry, King's College London, London SE5 8AF, United Kingdom;
                [7 ] Department of Psychology, The Hebrew University of Jerusalem, Jerusalem 91905, Israel;
                [8 ] Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, University of California, Los Angeles, CA 90095;
                [9 ] Social Science Research Institute, Duke University, Durham, NC 27708;
                [10 ] Department of Psychology, University of Otago, Dunedin 9016, New Zealand.
                Article
                1506264112
                10.1073/pnas.1506264112
                26150497

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